CN111611743A - Axial-flow compressor characteristic line self-adaption method - Google Patents

Axial-flow compressor characteristic line self-adaption method Download PDF

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CN111611743A
CN111611743A CN202010411884.5A CN202010411884A CN111611743A CN 111611743 A CN111611743 A CN 111611743A CN 202010411884 A CN202010411884 A CN 202010411884A CN 111611743 A CN111611743 A CN 111611743A
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徐超
吴建平
陈健
钱江
郑隆云
应雨龙
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Shanghai Shangdian Electric Power Engineering Co ltd
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    • G06F30/20—Design optimisation, verification or simulation
    • G06F30/25—Design optimisation, verification or simulation using particle-based methods
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F04—POSITIVE - DISPLACEMENT MACHINES FOR LIQUIDS; PUMPS FOR LIQUIDS OR ELASTIC FLUIDS
    • F04D—NON-POSITIVE-DISPLACEMENT PUMPS
    • F04D19/00—Axial-flow pumps
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Abstract

The invention relates to a characteristic line self-adaption method of an axial-flow compressor, which is characterized in that a thermodynamic model of a multistage axial-flow compressor is established through a step-by-step superposition calculation method according to a general-stage characteristic curve calculation formula of each stage of the axial-flow compressor; the method comprises the steps of collecting gas path measurement parameters of a gas turbine of an actual object, taking root mean square error between the gas path parameters obtained through calculation of a thermal model and gas path measurement data obtained through collection of the gas turbine of the actual object as a target function, iteratively optimizing shape coefficient vectors of all levels of general level characteristic curves in the thermal model through a particle swarm optimization algorithm, and generating an axial flow type compressor general characteristic line matched with the compressor characteristics of the gas turbine of the actual object. Thereby eliminating the uncertainty introduced by manufacturing and mounting deviation among different combustion engines of the same type; the uncertainty introduced by different interferences and unknown initial conditions is eliminated; and errors caused by the assumed conditions are calculated in a step-by-step overlapping mode during generation of the component characteristic lines, so that the accuracy of the component characteristic lines of the gas compressor is improved.

Description

Axial-flow compressor characteristic line self-adaption method
Technical Field
The invention relates to an energy and power engineering technology, in particular to a characteristic line self-adaption method of an axial-flow compressor.
Background
Under the current thermal modeling technical condition of the gas turbine, the accuracy of a thermal model mainly depends on the characteristic line of parts (a gas compressor and a turbine) of the thermal model and the accuracy degree of a working medium thermophysical property calculation program, particularly the accuracy of the gas compressor characteristic line. These component property lines are actually obtained by rigorous testing of the engine test bed under different operating conditions, or by Computational Fluid Dynamics (CFD) numerical simulation. Since the test bed tests are time consuming and expensive, it is not possible for the engine manufacturer to obtain part characterization lines for each gas turbine. Thus, manufacturers typically only provide a user with a set of component property lines for the same model gas turbine engine. However, for the same model of gas turbine, there are some differences in the characteristics of the components due to manufacturing and assembly variations. In addition, the characteristics of the components may also be greatly changed due to maintenance, modification, overhaul, and the like. Therefore, when thermal modeling is performed using the same set of component property lines for the same model of gas turbine, a certain degree of calculation error is usually generated. For users, sometimes, the component characteristic lines of the relevant models of gas turbines cannot be obtained even for various reasons of manufacturers, and the components can only be scaled and used by the component characteristic lines of other existing gas turbines, so that the thermodynamic calculation error is sometimes unacceptable. In this case, the step-by-step superposition calculation method becomes a reliable and effective means for generating the characteristic line of the compressor, and the calculation process is mainly based on the continuous mean radius of one dimensionA sexual flow equation and a set of general stage characteristics. For the user, the geometric parameters of each blade stage of the actual unit compressor are not known, so the step-by-step superposition calculation method can only adopt a set of general stage characteristic curves obtained by fitting a large amount of existing blade stage test data (the general stage characteristic curves of the compressor illustrate the compressor stage relative pressure coefficients ψ (ψ/ψ) respectively as shown in fig. 10) And a relative flow coefficient phi (phi is phi/phi)0＝(Ca/u)/(Ca/u)0) And relative isentropic efficiency η (η ═ η/η)0) And relative flow coefficient phi) to characterize the stage characteristics of each stage of the actual compressor, wherein psi is a pressure coefficient; phi is a flow coefficient; caIs the airflow axial velocity; u is the peripheral speed of the mean radius of the blade; subscript 0 is the design operating point. Because of the simplified conditions of the compressor characteristic line generation process, the thermodynamic model of the gas turbine established by the step-by-step superposition calculation method has inevitable errors to a certain extent, and the precision of the component characteristic line is insufficient.
Disclosure of Invention
The invention provides an axial-flow compressor characteristic line self-adaption method aiming at the problem of insufficient precision of a characteristic line of a compressor generated in the prior art, wherein a root mean square error between gas circuit measurement data of an actual gas turbine and gas circuit parameters calculated by a thermal model is taken as an objective function, a group of shape coefficient vectors of a stage characteristic curve of each stage of the compressor are obtained through iterative optimization calculation of a particle swarm optimization algorithm, a general characteristic line of the axial-flow compressor matched with the compressor characteristic of the actual gas turbine is generated, and therefore errors caused by step-by-step superposition calculation assumed conditions when a component characteristic line is generated are eliminated.
The technical scheme of the invention is as follows: an adaptive method for the characteristic line of axial-flow compressor includes such steps as adaptive control to the characteristic line of axial-flow compressor to obtain the characteristic line of axial-flow compressor, and adaptive control to the characteristic line of axial-flow compressor to obtain the characteristic line of axial-flow compressor]Randomly generating a set of shape coefficient vectors [ SF ] of the characteristic curves of the general stages in each stage within the numerical range1,SF2,...,SFm]M represents the number of stages of the compressor; then establishing the multistage axial-flow compressor by a step-by-step superposition calculation method according to a general-stage characteristic curve calculation formula of each stage of the axial-flow compressorA thermal model; collecting gas path measurement parameters of a gas turbine of an actual object, taking root mean square error between the gas path parameters obtained by calculation of the thermal model and gas path measurement data obtained by collection of the gas turbine of the actual object as a target function, and iteratively optimizing shape coefficient vectors [ SF ] of all levels of universal level characteristic curves in the thermal model by a particle swarm optimization algorithm1,SF2,...,SFm]And finally, generating an axial-flow compressor general characteristic line matched with the compressor characteristic of the actual object gas turbine through the optimal shape coefficient vector.
The general-level characteristic curves of all levels refer to general-level characteristic curves of all blade levels, and comprise a subsonic level, a transonic level and an ultrasonic level.
The invention has the beneficial effects that: the self-adaptive method for the characteristic line of the axial-flow type gas compressor eliminates errors caused by the step-by-step superposition calculation assumed conditions when the characteristic line of a part is generated, so that the accuracy of the characteristic line of the part of the gas compressor is improved, and the accuracy of a thermal model of the built gas turbine is improved.
Drawings
FIG. 1 is a general stage characteristic graph for a compressor;
FIG. 2 is a schematic diagram of the characteristic line adaptive method of the axial flow compressor of the present invention;
FIG. 3 is a general characteristic diagram of the flow characteristics of the compressor generated after the adaptation of the present invention;
fig. 4 is a general characteristic diagram of the compressor efficiency characteristics generated after the adaptation of the present invention.
Detailed Description
Stage-stacking is a reliable and efficient means for generating compressor profiles, based mainly on a continuous flow equation at one-dimensional mean radius and a set of general-purpose Stage profiles. For users, geometric parameters of each blade stage of the actual compressor unit are generally unknown, so that the step-by-step superposition calculation method can only adopt a set of general stage characteristic curve obtained by fitting a large amount of existing blade stage test data to represent the stage characteristics of each stage of the actual compressor unit, and neglect the blade stages of different stage types(e.g., subsonic, transonic, and supersonic) inherently have different stage characteristics. The method takes the root mean square error between gas path measurement data of the actual gas turbine and gas path parameters calculated by a thermodynamic model as an objective function, and obtains the shape coefficient vector SF ═ SF of the stage characteristic curve of each stage of the gas compressor through iterative optimization calculation of a particle swarm optimization algorithm1,SF2,...,SFi,...,SFm]And (m represents the number of stages of the compressor), and generating an axial-flow compressor general characteristic line matched with the compressor characteristic of the actual gas turbine, so that the error caused by the step-by-step superposition calculation assumed condition when the component characteristic line is generated is eliminated, the accuracy of the compressor component characteristic line is improved, and the accuracy of the established gas turbine thermal model is improved.
The specific technical scheme is as follows:
as shown in fig. 2, first at [ -0.5,1 [ ]]Randomly generating a set of shape coefficient vectors [ SF ] of the characteristic curves of the general stages in each stage within the numerical range1,SF2,...,SFm]And then, according to the general stage characteristic curves of all stages of the axial-flow compressor, calculating formulas (1) and (2) and establishing a thermodynamic model of the multistage axial-flow compressor by a step-by-step superposition calculation method. Collecting gas path measurement parameters of a gas turbine of an actual object, taking root mean square error between the gas path parameters obtained by calculation of the thermal model and gas path measurement data obtained by collection of the gas turbine of the actual object as a target function, and iteratively optimizing shape coefficient vectors [ SF ] of all levels of universal level characteristic curves in the thermal model by a particle swarm optimization algorithm1,SF2,...,SFm]Finally, generating an axial-flow compressor general characteristic line (a flow characteristic line and an efficiency characteristic line) matched with the compressor characteristic of an actual object gas turbine through an optimal shape coefficient vector, and eliminating 3-aspect uncertainty, wherein ① eliminates uncertainty introduced by manufacturing and installation deviation among different combustion engines of the same type, ② eliminates uncertainty introduced by different interference and unknown initial conditions, and errors caused by step-by-step superposition calculation of assumed conditions during generation of ③ component characteristic lines are added, so that the accuracy of the compressor component characteristic line is improved, and the accuracy of the established gas turbine thermal model is improved.
The general stage characteristic curves for the individual stages of an axial compressor can be represented by the formulae (1) and (2):
Figure BDA0002493562180000041
Figure BDA0002493562180000042
in the formula
Figure BDA0002493562180000043
The i-stage relative pressure coefficient of the compressor is obtained;
Figure BDA0002493562180000044
and the relative flow coefficient of the i-stage of the compressor. By definition of the pressure coefficient of the stage
Figure BDA0002493562180000045
Definition of the flow coefficient of the sum stage ═ CaThe relative pressure coefficient of a certain stage can be obtained by a step-by-step superposition calculation method
Figure BDA0002493562180000046
And the relative flow coefficient of the stage
Figure BDA0002493562180000047
Here, the
Figure BDA0002493562180000048
The inlet specific enthalpy of the i-stage is expressed,
Figure BDA0002493562180000049
expressing the outlet ideal specific enthalpy, R, of stage igWhich represents the gas constant of the air,
Figure BDA00024935621800000410
the total inlet temperature of the i stage is shown,
Figure BDA00024935621800000411
denotes the total inlet pressure, G, of stage ii,inRepresenting the inlet air mass flow at level i; the subscript 0 indicates the design condition.
SFiFor the shape factor of the class characteristic curve of class i, by adjusting SFiCan characterize each of the different stage types (e.g., subsonic, transonic, and supersonic), here as variables to be optimized.
Wherein the specific expansion of equation (1) is:
Figure BDA00024935621800000412
in the formula (I), the compound is shown in the specification,
Figure BDA00024935621800000413
the maximum value of the relative pressure coefficient of the i-th stage of the compressor is obtained;
Figure BDA00024935621800000414
the method is characterized by comprising the step of obtaining a corresponding relative flow coefficient when the maximum value of the i-th stage relative pressure coefficient of the compressor is obtained.
The specific expansion of equation (2) is:
when in use
Figure BDA0002493562180000051
When the temperature of the water is higher than the set temperature,
Figure BDA0002493562180000052
when in use
Figure BDA0002493562180000053
When the temperature of the water is higher than the set temperature,
Figure BDA0002493562180000054
in the formula (I), the compound is shown in the specification,
Figure BDA0002493562180000055
is composed of
Figure BDA0002493562180000056
Taking the corresponding relative isentropic efficiency when the minimum value is obtained;
Figure BDA0002493562180000057
is composed of
Figure BDA0002493562180000058
And taking the corresponding relative isentropic efficiency when the maximum value is taken. Here, the
Figure BDA0002493562180000059
Taken as 0.04, corresponding
Figure BDA00024935621800000510
Taking the value as 0.20;
Figure BDA00024935621800000511
taken as 1.46, corresponding
Figure BDA00024935621800000512
Taken to be 0.92.
Establishing a compressor thermodynamic model based on the equations (1) to (5), wherein the overall general characteristic line of the compressor thermodynamic model can be represented by the following equations (6) and (7):
Gcor,rel＝f1(ncor,rel,πC,rel) (6)
ηC,rel＝f2(ncor,rel,πC,rel) (7)
in the formula:
Figure BDA00024935621800000513
the relative reduced rotation speed of the compressor;
Figure BDA00024935621800000514
the relative reduced flow of the compressor;
Figure BDA00024935621800000515
η relative pressure ratio of compressorC,rel＝ηC/ηC0As relative isentropic of the compressorEfficiency; the lower corner mark 0 represents the design condition; n represents a rotation speed;
Figure BDA00024935621800000516
the total temperature of the inlet of the compressor; rgIs the gas constant of air; g is the mass flow of air;
Figure BDA00024935621800000517
the total pressure of the inlet of the compressor is measured; piCThe compressor pressure ratio.
Then, the root mean square error between gas path measurement data (including inlet and outlet total temperature, total pressure, rotating speed, atmospheric temperature, pressure and relative humidity of the target compressor) of the actual gas turbine and corresponding gas path parameters calculated by the established compressor thermodynamic model is taken as an objective function, and the shape coefficient vector [ SF ] of the stage characteristic curve of each stage of the compressor is obtained through iterative optimization calculation of a particle swarm optimization algorithm1,SF2,...,SFi,...,SFm]And generating an axial-flow compressor general characteristic line matched with the compressor characteristic of the actual gas turbine, thereby eliminating errors caused by step-by-step superposition calculation hypothesis conditions when the component characteristic line is generated, improving the accuracy of the compressor component characteristic line and improving the accuracy of the established gas turbine thermal model.
The value range of the initial iterative shape coefficient of the particle swarm optimization algorithm is [ -0.5,1], m is the total number of stages of the axial-flow compressor, and finally, by iterative optimization, taking a certain type of gas turbine as an example, the adaptive general characteristic curve cluster of the compressor shown in fig. 3 and 4 can be generated.

Claims (2)

1. An adaptive method for characteristic line of axial-flow compressor is characterized by firstly setting the characteristic line of axial-flow compressor at [ -0.5,1]Randomly generating a set of shape coefficient vectors [ SF ] of the characteristic curves of the general stages in each stage within the numerical range1,SF2,...,SFm]M represents the number of stages of the compressor; then, according to a general stage characteristic curve calculation formula of each stage of the axial-flow compressor, a thermodynamic model of the multistage axial-flow compressor is established through a stage-by-stage superposition calculation method; gas turbine with actual object acquisitionThe gas path measurement parameters are obtained by taking the root mean square error between the gas path parameters obtained by calculation of the thermal model and the gas path measurement data obtained by collection of the actual object gas turbine as a target function, and the shape coefficient vector [ SF ] of each-stage general-stage characteristic curve in the thermal model is iteratively optimized by a particle swarm optimization algorithm1,SF2,...,SFm]And finally, generating an axial-flow compressor general characteristic line matched with the compressor characteristic of the actual object gas turbine through the optimal shape coefficient vector.
2. The adaptive method for characteristic lines of an axial-flow compressor according to claim 1, wherein the characteristic curves of all stages refer to characteristic curves of all stages of blades, including subsonic, transonic and supersonic stages.
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CN118377921A (en) * 2024-06-21 2024-07-23 浙江大学 Self-adaptive generation method of full-working-condition characteristic curve of gas turbine component

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CN114444331A (en) * 2022-04-07 2022-05-06 中国航发四川燃气涡轮研究院 Stage characteristic matching method of multistage axial flow compressor
CN118377921A (en) * 2024-06-21 2024-07-23 浙江大学 Self-adaptive generation method of full-working-condition characteristic curve of gas turbine component

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